As the volume and variety of information available to information recipients has increased, the ability to filter signal information from noise information in such an ever-expanding volume and variety has become increasingly important. For example, an information recipient may receive one hundred email messages per day, of which ten are urgent, seventy five are regular email messages (not urgent) and fifteen are junk mail (“spam”) and thus not urgent. The ability to classify such messages, to produce, for example, a list ordered on urgency, can increase productivity of the information recipient by facilitating prioritizing and focusing on important messages.
Conventionally, static information classifiers have been produced that facilitate classifying information on parameters including, but not limited to, urgency, relevance to task, likelihood of being spam and likelihood of being of interest to an information recipient. But static information classifiers suffer from the problem that one man's garbage is another man's gold. Thus, what may be spam to a first information recipient may be an urgent message to a second information recipient. More generally, different information recipients may have different information receiving goals and priorities, which pre-trained classification systems may not be able to accommodate.
Thus there remains a need for a system and method to improve classification systems to facilitate accounting for personal attributes associated with the information recipient (e.g., preferences, usage patterns, task at hand) and/or individuating attributes of the received information.